TEMPORAL AND SPATIAL PATTERNS IN DAILY MASS GAIN OF MAGNOLIA WARBLERS DURING MIGRATORY STOPOVER
Bibliographic record
Abstract
Whether or not migrants gain mass at a stopover site is an index of site quality. Previous studies have examined mass gain of recaptured birds, and of short-term stopovers by regressing mass at first capture on hour of day. I developed an extension of the latter method using multiple regression to examine the effects on mass gain of hour of day, date, and year. I then used the method to compare the quality of three stopover sites at Long Point, Ontario, for Magnolia Warblers (Dendroica magnolia). At the peak of fall migration, warblers at all three sites gained sufficient mass for a net gain over 24 h, but they gained mass at only two of three sites during spring. Mass gain varied significantly over the course of the day, by date in the season, and among years. The earliest spring migrants lost mass at all sites, but rate of mass gain increased as the season progressed. Similar information for many more species and stopover sites might aid in habitat conservation for migrants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".